Journal of the American Statistical Association

最新文章

• Instrumental Variable Estimators for Binary Outcomes AI翻译
  作者：Clarke, Paul S.; Windmeijer, Frank
  作者单位：University of Bristol; University of Bristol; University of Bristol
  摘要：Instrumental variables (IVs) can be used to construct estimators of exposure effects on the outcomes of studies affected by nonignorable selection of the exposure. Estimators that fail to adjust for the effects of nonignorable selection will be biased and inconsistent. Such situations commonly arise in observational studies, but are also a problem for randomized experiments affected by nonignorable noncompliance. In this article, we review IV estimators for studies in which the outcome is bina...
• Modeling Protein Expression and Protein Signaling Pathways AI翻译
  作者：Telesca, Donatello; Mueller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of Texas System; University of Texas Austin; University of Texas System; UTMD Anderson Cancer Center
  摘要：High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying...
• Cross-Dimensional Inference of Dependent High-Dimensional Data AI翻译
  作者：Desai, Keyur H.; Storey, John D.
  作者单位：Princeton University; Princeton University
  摘要：A growing number of modern scientific problems in areas such as genomics, neurobiology, and spatial epidemiology involve the measurement and analysis of thousands of related features that may be stochastically dependent at arbitrarily strong levels. In this work, we consider The scenario where the features follow a multivariate Normal distribution. We demonstrate that dependence is manifested as random variation shared among features, and that standard methods may yield highly unstable inferen...
• MRI Tissue Classification Using High-Resolution Bayesian Hidden Markov Normal Mixture Models AI翻译
  作者：Feng, Dai; Tierney, Luke; Magnotta, Vincent
  作者单位：Merck & Company; University of Iowa; University of Iowa
  摘要：Magnetic resonance imaging (MRI) is used to identify the major tissues within a subject's brain. Classification is usually based on a single image providing one measurement for each volume element, or voxel, in a discretization of the brain. A simple model views each voxel as homogeneous, belonging entirely to one of the three major tissue types: gray matter, white matter, or cerebrospinal fluid. The measurements are normally distributed, with means and variances depending on the tissue types ...
• Estimation of High Conditional Quantiles for Heavy-Tailed Distributions AI翻译
  作者：Wang, Huixia Judy; Li, Deyuan; He, Xuming
  作者单位：North Carolina State University; Fudan University; University of Michigan System; University of Michigan
  摘要：Estimation of conditional quantiles at very high or low tails is of interest in numerous applications. Quantile regression provides a convenient and natural way of quantifying the impact of covariates at different quantiles of a response distribution. However, high tails are often associated with data sparsity, so quantile regression estimation can suffer from high variability at tails especially for heavy-tailed distributions. In this article, we develop new estimation methods for high condit...
• Nonparametric Estimation for Censored Mixture Data With Application to the Cooperative Huntington's Observational Research Trial AI翻译
  作者：Wang, Yuanjia; Garcia, Tanya P.; Ma, Yanyuan
  作者单位：Columbia University; Texas A&M University System; Texas A&M University College Station
  摘要：This work presents methods for estimating genotype-specific outcome distributions from genetic epidemiology studies where the event times are subject to right censoring, the genotypes are not directly observed, and the data arise from a mixture of scientifically meaningful subpopulations. Examples of such studies include kin-cohort studies and quantitative trait locus (QTL) studies. Current methods for analyzing censored mixture data include two types of nonparametric maximum likelihood estima...
• Moderate-Deviation-Based Inference for Random Degeneration in Paired Rank Lists AI翻译
  作者：Hall, Peter; Schimek, Michael G.
  作者单位：University of Melbourne; University of Graz
  摘要：Consider a problem where N items (objects or individuals) are judged by assessors using their perceptions of a set of performance criteria, or alternatively by technical devices. In particular, two assessors might rank the items between 1 and N on the basis of relative performance, independently of each other. We can aggregate the rank lists by assigning one if the two assessors agree, and zero otherwise, and we can modify this approach to make it robust against irregularities. In this article...
• Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach AI翻译
  作者：Bevilacqua, Moreno; Gaetan, Carlo; Mateu, Jorge; Porcu, Emilio
  作者单位：Universita Ca Foscari Venezia; Universitat Jaume I; University of Gottingen
  摘要：In this article, we propose two methods for estimating space and space-time covariance functions from a Gaussian random field, based on the composite likelihood idea. The first method relies on the maximization of a weighted version of the composite likelihood function, while the second one is based on the solution of a weighted composite score equation. This last scheme is quite general and could be applied to any kind of composite likelihood. An information criterion for model selection base...
• Estimation of Copula Models With Discrete Margins via Bayesian Data Augmentation AI翻译
  作者：Smith, Michael S.; Khaled, Mohamad A.
  作者单位：University of Melbourne; University of Technology Sydney
  摘要：Estimation of copula models with discrete margins can be difficult beyond the bivariate case. We show how this can be achieved by augmenting the likelihood with continuous latent variables, and computing inference using the resulting augmented posterior. To evaluate this, we propose two efficient Markov chain Monte Carlo sampling schemes. One generates the latent variables as a block using a Metropolis Hastings step with a proposal that is close to its target distribution, the other generates ...
• Nonparametric Covariate-Adjusted Association Tests Based on the Generalized Kendall's Tau AI翻译
  作者：Zhu, Wensheng; Jiang, Yuan; Zhang, Heping
  作者单位：Yale University; Yale University; Sun Yat Sen University
  摘要：Identifying the risk factors for comorbidity is important in psychiatric research. Empirically, studies have shown that testing multiple correlated traits simultaneously is more powerful than testing a single trait at a time in association analysis. Furthermore, for complex diseases, especially mental illnesses and behavioral disorders, the traits are often recorded in different scales, such as dichotomous, ordinal, and quantitative. In the absence of covariates, nonparametric association test...